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Get group fair price

get_group_fair_price
Read-only

Use to check what a product of a given kind typically costs: give a task or product type (or a group_id from the group price league) and get its product group's fair price for each buyer tier (individual, pro, team / small business, enterprise) — the median entry monthly price in that tier with its typical range — plus which payment models the group typically uses (free, subscription, usage, one-time, contact sales), usage and one-time medians where at least 3 listings publish them, and the group's price-index change over 90 days. Groups are built from what products do, not from price. Medians of current published prices only; individual listings are not returned — use search_providers for candidates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoThe task or product type, e.g. 'AI meeting notes for sales teams'.
group_idNoOptional: a group id from agentery.com/aepi/groups or from get_provider_profile's product_group.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld annotations, it discloses the data semantics that matter: medians of current published prices only, no raw listings, group membership defined by product behavior rather than price, usage/one-time medians only where at least 3 listings publish them, and a 90-day price-index window. These are material behavioral facts not derivable from annotations or schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded and the closing sentences route the agent efficiently. The middle is a single very long em-dash-chained sentence enumerating outputs, which is information-dense but harder to parse than it needs to be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of explaining returns — buyer tiers, median entry monthly price with range, payment models, usage/one-time medians, and price-index change — and it does so thoroughly, while annotations cover the safety profile.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning: it clarifies that query and group_id are alternative entry points and points to two concrete sources for group_id (agentery.com/aepi/groups and get_provider_profile's product_group), which the schema does not state.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a precise verb+resource+scope: retrieve a product group's fair price broken out by buyer tier. It also distinguishes itself from search_providers, explicitly noting that individual listings are not returned. An agent can identify the tool's job without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Opens with an explicit trigger ('Use to check what a product of a given kind typically costs') and names an alternative path (search_providers) for candidate-level results. It does not, however, differentiate itself from adjacent siblings like price_benchmark or get_price_index, which an agent might reasonably confuse it with.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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